Title

Authors

Published In

IFAC-PapersOnLine

Document Type

Citation

Publication Date

12-22-2016

Abstract

An innovation-weight parametrization is introduced as a practical approach to account for deficiencies in the representation of both background error and observation error covariance in a variational data assimilation system. The adjoint-based evaluation of the forecast error sensitivity provides a computationally efficient diagnosis to observation-space distributed parameters and guidance for tuning the analysis Kalman gain operator. Theoretical aspects are discussed and preliminary results are presented with the adjoint versions of the Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) and the Navys Global Environmental Model (NAVGEM).